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Projection: This operator results in a table with fewer columns, based on a specified set of attributes you want to see in the result. In other words, the result is a vertical subset of the input table. Union: This operator merges the rows of two input tables into a single output table; the result contains all rows that occur in at least one of the input tables. Intersection: This operator also accepts two input tables; the result consists of all rows that occur in both input tables. Minus: Again, based on two input tables, this operator produces a result that consists of those rows that occur in the first table but do not occur in the second table. Note that this operator is not symmetric; A MINUS B is not the same as B MINUS A. This operator is also referred to as difference. (Cartesian) product: From two input tables, all possible combinations are generated by concatenating a row from the first table with a row from the second table. (Natural) Join: From two input tables, one result table is produced. The rows in the result consist of all combinations of a row from the first table with a row from the second table, provided both rows have identical values for the common attributes.
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The natural join is an example of an operator that is not strictly necessary, because the effect of this operator can also be achieved by applying the combination of a Cartesian product, followed by a restriction (to check for identical values on the common attributes), and then followed by a projection to remove the duplicate columns.
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1.7 How Relational Is My DBMS
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The term relational is used (and abused) by many DBMS vendors these days. If you want to determine whether these vendors speak the truth, you are faced with the problem that relational is a theoretical concept. There is no simple litmus test to check whether or not a DBMS is relational. Actually, to be honest, there are no pure relational DBMS implementations. That s why it is better to investigate the relational degree of a certain DBMS implementation. This problem was identified by Ted Codd, too; that s why he published 12 rules (actually, there are 13 rules, if you count rule zero, too) for relational DBMS systems in 1986. Since then, these rules have been an important yardstick for RDBMS vendors. Without going into too much detail, Codd s rules are listed here, with brief explanations: 0. 1. 2. Rule Zero: For any DBMS that claims to be relational, that system must be able to manage databases entirely through its relational capabilities. The Information Rule: All information in a relational database is represented explicitly at the logical level and in exactly one way: by values in tables. Guaranteed Access Rule: All data stored in a relational database is guaranteed to be logically accessible by resorting to a combination of a table name, primary key value, and column name.
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RELATIONAL DATABASE SYSTEMS AND ORACLE
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Systematic Treatment of Missing Information: Null values (distinct from the empty string, blanks, and zero) are supported for representing missing information and inapplicable information in a systematic way, independent of the datatype. Dynamic Online Catalog: The database description is represented at the logical level in the same way as ordinary data, so that authorized users can apply the same relational language to its interrogation as they apply to the regular data. Comprehensive Data Sublanguage: There must be at least support for one language whose statements are expressible by some well-defined syntax and comprehensive in supporting all of the following: data definition, view definition, data manipulation, integrity constraints, authorization, and transaction boundaries handling. Updatable Views: All views that are theoretically updatable are also updatable by the system. High-Level Insert, Update, and Delete: The capability of handling a table or a view as a single operand applies not only to the retrieval of data, but also to the insertion, updating, and deletion of data. Physical Data Independence: Application programs remain logically unimpaired whenever any changes are made in either storage representations or access methods. Logical Data Independence: Application programs remain logically unimpaired when information-preserving changes that theoretically permit unimpairment are made to the base tables.
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10. Integrity Independence: Integrity constraints must be definable in the relational data sublanguage and storable in the catalog, not in the application programs. 11. Distribution Independence: Application programs remain logically unimpaired when data distribution is first introduced or when data is redistributed. 12. The Nonsubversion Rule: If a relational system also supports a low-level language, that low-level language cannot be used to subvert or bypass the integrity rules and constraints expressed in the higher-level language. Rule 5 refers to transactions. Without going into too much detail here, a transaction is defined as a number of changes that should be treated by the DBMS as a single unit of work; a transaction should always succeed or fail completely. For further reading, please refer to Oracle Insights: Tales of the Oak Table by Dave Ensor (Apress, 2004), especially 1.
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